2020/05/13 by Sharon Xu, Sharon Ying Xu, Xu, Sharon +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Environmental Science · Mathematics · Physics and Astronomy · #Diffusion and Search Dynamics #Ecosystem dynamics and resilience #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Point processes and geometric inequalities #Social and Information Networks (cs.SI) #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.2005.06542
arxiv created 2020/05/13 · openalex publication_date 2020/05/13 · arxiv updated 2020/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Human behavior drives a range of complex social, urban, and economic systems, yet understanding its structure and dynamics at the individual level remains an open question. From credit card transactions to communications data, human behavior appears to exhibit bursts of activity driven by task prioritization and periodicity, however, current research does not offer generative models capturing these mechanisms. We propose a multivariate, periodic Hawkes process (MPHP) model that captures -- at the individual level -- the temporal clustering of human activity, the interdependence structure and co-excitation of different activities, and the periodic effects of weekly rhythms. We also propose a scalable parameter estimation technique for this model using maximum-aposteriori expectation-maximization that additionally provides estimation of latent variables revealing branching structure of an individual's behavior patterns. We apply the model to a large dataset of credit card transactions, and demonstrate the MPHP outperforms a non-homogeneous Poisson model and LDA in both statistical fit for the distribution of inter-event times and an activity prediction task.